





# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_lowerCamelCase 					=      'tiny-wmt19-en-ru'

# Build

# borrowed from a test
_lowerCamelCase 					=      [
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
_lowerCamelCase 					=      dict(zip(vocab, range(len(vocab))))
_lowerCamelCase 					=      ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
	_lowerCamelCase 					=      Path(tmpdirname)
	_lowerCamelCase 					=      build_dir / VOCAB_FILES_NAMES['src_vocab_file']
	_lowerCamelCase 					=      build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
	_lowerCamelCase 					=      build_dir / VOCAB_FILES_NAMES['merges_file']
	with open(src_vocab_file, 'w') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(tgt_vocab_file, 'w') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(merges_file, 'w') as fp:
		fp.write('\n'.join(merges))

	_lowerCamelCase 					=      FSMTTokenizer(
	    langs=['en', 'ru'],
	    src_vocab_size=len(vocab),
	    tgt_vocab_size=len(vocab),
	    src_vocab_file=src_vocab_file,
	    tgt_vocab_file=tgt_vocab_file,
	    merges_file=merges_file,
	)

_lowerCamelCase 					=      FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_lowerCamelCase 					=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase 					=      tokenizer(['Making tiny model'], return_tensors='pt')
_lowerCamelCase 					=      tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


__lowerCAmelCase     :	Optional[Any]       			=  'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			_ask_options(
      """In which compute environment are you running?""",	["""This machine""", """AWS (Amazon SageMaker)"""],	_convert_compute_environment,	)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    __magic_name__								=			get_sagemaker_input()
  else:
    __magic_name__								=			get_cluster_input()
  return config

def a__       (			A_=None   ):






  '''simple docstring'''





  if subparsers is not None:
    __magic_name__								=			subparsers.add_parser("""config""",	description=A_   )
  else:
    __magic_name__								=			argparse.ArgumentParser("""Accelerate config command""",	description=A_   )

  parser.add_argument(
      """--config_file""",	default=A_,	help=(
          """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      ),	)

  if subparsers is not None:
    parser.set_defaults(func=A_   )
  return parser

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			get_user_input()
  if args.config_file is not None:
    __magic_name__								=			args.config_file
  else:
    if not os.path.isdir(A_   ):
      os.makedirs(A_   )
    __magic_name__								=			default_yaml_config_file

  if config_file.endswith(""".json"""   ):
    config.to_json_file(A_   )
  else:
    config.to_yaml_file(A_   )
  print(f'''accelerate configuration saved at {config_file}'''   )

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			config_command_parser()
  __magic_name__								=			parser.parse_args()
  config_command(A_   )


if __name__ == "__main__":
    main()

